Drug constant temperature system control optimization method

Abstract

The invention discloses a medicine constant temperature system control optimization method, and mainly relates to the technical field of medicine constant temperature control, and the method comprises the steps: obtaining a pre-arranged temperature sensor array of a target medicine constant temperature area; obtaining a temperature data sub-sequence cluster; obtaining a key temperature value sequence set corresponding to the temperature data sequence set; constructing a key temperature value topology network sequence; performing iterative correlation analysis on the key temperature value topology network sequence according to a time sequence, and determining iterative correlation temperature features; and performing control optimization on the medicine constant temperature system based on the iterative correlation temperature characteristics to obtain control optimization parameters. According to the invention, the technical problems of low control accuracy and slow system response caused by lack of in-depth analysis of temperature in medicine constant temperature system control optimization in the prior art are solved, and the technical effects of effectively reducing the processing complexity of temperature data and improving the accuracy of the temperature control system are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of medicine constant temperature control, and in particular to a medicine constant temperature system control optimization method. Background Art

[0002] Traditional drug constant temperature control systems usually rely on temperature sensors to monitor the temperature of drug storage areas in real time to ensure temperature stability. During the temperature monitoring process, a large amount of temperature data usually needs to be processed and analyzed to ensure that the drug storage environment meets the specified temperature requirements. However, since it often relies on a single real-time temperature measurement, there is a lack of in-depth analysis of temperature change trends, and it is impossible to perform efficient correlation analysis on temperature data over a period of time, which may cause the temperature control system to respond slowly or inaccurately, thereby affecting the quality and safety of drugs.

[0003] The existing technology has the technical problem that the optimization of the drug constant temperature system control lacks in-depth analysis of temperature, resulting in low control accuracy and slow system response. Summary of the invention

[0004] The present application provides a method for optimizing the control of a medicine constant temperature system, which is used to solve the technical problem in the prior art that the control optimization of a medicine constant temperature system lacks in-depth analysis of temperature, resulting in low control accuracy and slow system response.

[0005] In view of the above problems, the present application provides a method for optimizing the control of a medicine constant temperature system, the method comprising:

[0006] Acquire a pre-placed temperature sensor array of a target drug constant temperature area;

[0007] Traversing the temperature sensor array to extract temperature data within a preset time period to obtain a temperature data sequence set, and presetting a fixed time window, using the preset fixed time window to perform sequence segmentation processing on the temperature data sequence set to obtain a temperature data subsequence cluster, wherein the temperature data subsequence cluster includes a temperature data subsequence set corresponding to each temperature data sequence;

[0008] Performing subsequence key temperature value screening on each temperature data subsequence in the temperature data subsequence cluster respectively, and performing serialization processing on the screening results to obtain a key temperature value sequence set corresponding to the temperature data sequence set;

[0009] Based on the position of each temperature sensor in the temperature sensor array, performing simultaneous topological processing on the key temperature value sequence set to construct a key temperature value topological network sequence;

[0010] Performing iterative correlation analysis on the key temperature value topological network sequence in chronological order to determine iterative correlation temperature characteristics of the constant temperature area of ​​the target drug within a preset time period;

[0011] Based on the iterative correlation temperature characteristics, the drug constant temperature system is controlled and optimized to obtain control optimization parameters, wherein the drug constant temperature system is used to control the temperature of the target drug constant temperature area.

[0012] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0013] The present application obtains a pre-deployed temperature sensor array in a target drug constant temperature area, then traverses the temperature sensor array to extract temperature data within a preset time period, obtains a temperature data sequence set, and presets a fixed time window. The temperature data sequence set is subjected to sequence segmentation processing using the preset fixed time window to obtain a temperature data subsequence cluster, wherein the temperature data subsequence cluster includes a temperature data subsequence set corresponding to each temperature data sequence, and then, each temperature data subsequence in the temperature data subsequence cluster is screened for a subsequence key temperature value, and the screening results are serialized to obtain a key temperature value sequence set corresponding to the temperature data sequence set, and then, based on the position of each temperature sensor in the temperature sensor array, the key temperature value sequence set is subjected to simultaneous topological processing to construct a key temperature value topological network sequence, and the key temperature value topological network sequence is subjected to iterative correlation analysis in chronological order to determine the iterative correlation temperature characteristics of the target drug constant temperature area within the preset time period, and then, the drug constant temperature system is controlled and optimized based on the iterative correlation temperature characteristics to obtain control optimization parameters, wherein the drug constant temperature system is used to control the temperature of the target drug constant temperature area. The technical effect of effectively reducing the complexity of temperature data processing and improving the accuracy and response speed of the temperature control system through in-depth temperature data analysis is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Attached Figure 1 It is a flow chart of a method for optimizing control of a medicine constant temperature system provided in an embodiment of the present invention.

[0015] Attached Figure 2 It is a flow chart of obtaining iteratively correlated temperature characteristics in a drug constant temperature system control optimization method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0017] Embodiment, as attached Figure 1 As shown, the present application provides a method for optimizing the control of a medicine constant temperature system, wherein the method comprises:

[0018] S100: Acquire a pre-deployed temperature sensor array in a target drug constant temperature area;

[0019] In an embodiment of the present application, the target drug constant temperature area is an environmental area specially set for storing drugs, and its temperature needs to be maintained within a certain range to ensure the effectiveness and safety of the drugs. This area usually uses temperature control equipment (such as air conditioners, heaters, etc.) to maintain a stable temperature. The pre-deployed temperature sensor array is a plurality of temperature sensors that are planned and installed in advance in the target drug constant temperature area. The sensor array is to comprehensively monitor the temperature distribution in the area to ensure that the temperature at each location can be accurately monitored. Pre-deployment means that these sensors have been installed before the system is implemented, and the position has been optimized to ensure that they can effectively cover the entire target area.

[0020] The pre-deployed temperature sensor array can monitor the temperature in real time at various locations in the drug storage area. The data provided by the sensor array will help the system fully understand the temperature distribution of the drug storage environment to ensure that there are no temperature blind spots. This has achieved the technical effect of laying the foundation for the subsequent acquisition of data as a basis for analysis and optimization of the control system.

[0021] S200: traversing the temperature sensor array to extract temperature data within a preset time period to obtain a temperature data sequence set, and presetting a fixed time window, using the preset fixed time window to perform sequence segmentation processing on the temperature data sequence set to obtain a temperature data subsequence cluster, wherein the temperature data subsequence cluster includes a temperature data subsequence set corresponding to each temperature data sequence;

[0022] In one embodiment, each sensor in the temperature sensor array is accessed one by one to obtain their respective temperature data. By traversing the entire array, the temperature information of each position in the constant temperature area of ​​the target drug can be comprehensively collected. The preset time period refers to a time range set in advance during the data acquisition process. The temperature data within this time period will be extracted and used for subsequent analysis. The temperature data sequence set refers to a set of temperature data obtained from each temperature sensor, which are arranged in chronological order to form a data sequence. The data sequence of each sensor represents the temperature change of the sensor within a preset time period. The preset fixed time window is a time period of fixed length, which is used to segment the temperature data sequence. By setting a fixed time window, the entire temperature data sequence can be divided into multiple smaller subsequences according to a specific time step, thereby obtaining the temperature data subsequence cluster, wherein the temperature data subsequence cluster includes a temperature data subsequence set corresponding to each temperature data sequence.

[0023] After each temperature data sequence is segmented according to a fixed time window, the temperature data in each time period constitutes a subsequence, and multiple subsequences are combined into a temperature data subsequence set corresponding to a temperature data sequence.

[0024] By processing the temperature data obtained from the temperature sensor array into time periods and further segmenting the data, it is possible to obtain the temperature change information within the constant temperature area of ​​the target drug. This allows for a more detailed analysis of the temperature fluctuations within each time period and the discovery of possible temperature anomalies or trends. This provides a technical effect of providing a data basis for subsequent key temperature value screening, topological analysis, etc.

[0025] S300: Screening each temperature data subsequence in the temperature data subsequence cluster for a subsequence key temperature value, and performing serialization processing on the screening results to obtain a key temperature value sequence set corresponding to the temperature data sequence set;

[0026] Further, each temperature data subsequence in the temperature data subsequence cluster is screened for a subsequence key temperature value, and the screening result is serialized to obtain a key temperature value sequence set corresponding to the temperature data sequence set. Step S300 of the embodiment of the present application further includes:

[0027] Randomly extracting a first temperature data subsequence from the temperature data subsequence cluster, and constructing a screening space based on the first temperature data subsequence to obtain a temperature screening space, wherein the temperature screening space includes a plurality of temperature screening points, and each temperature screening point corresponds to a temperature data;

[0028] Extracting a central temperature screening point of the temperature screening space, taking the central temperature screening point as an index, screening a key temperature value in the temperature screening space according to a preset screening bandwidth, and obtaining a first key temperature value;

[0029] Performing subsequence key temperature value screening on each temperature data subsequence in the temperature data subsequence cluster to obtain a subsequence key temperature value cluster;

[0030] Each subsequence key temperature value set in the subsequence key temperature value cluster is serialized to obtain the key temperature value sequence set.

[0031] In one embodiment, the most representative temperature values ​​are selected from each temperature data subsequence. These key temperature values ​​can reflect important information in the temperature data, such as peak values, valley values, and change trends, and the screening results are serialized in chronological order to obtain the key temperature value sequence set. The purpose of the screening process is to extract representative key temperature values ​​from the temperature data, thereby achieving the technical effect of reducing the dimension of the data, improving the efficiency of data analysis, and thus improving the system control optimization response rate.

[0032] The key temperature value sequence set refers to a set formed by arranging the key temperature values ​​selected from the temperature data subsequence cluster in a time sequence from the beginning to the end. Each temperature data subsequence corresponds to a key temperature value.

[0033] Preferably, the first temperature data subsequence is randomly extracted from the temperature data subsequence cluster to construct the temperature screening space. The temperature screening space is obtained by inputting the selected first temperature data subsequence into a two-dimensional coordinate system. The screening space includes multiple temperature screening points, each of which represents a specific temperature value.

[0034] The central temperature screening point refers to a representative or important temperature screening point in the screening space, which is usually used as an index point for further screening operations. Optionally, the central temperature screening point can be the center point of the space. The preset screening bandwidth refers to a temperature range set during the screening process to ensure that the selected temperature data points have a certain relevance and representativeness. The bandwidth controls the looseness of the screening, that is, the range of temperature data deviation allowed during the screening.

[0035] Optionally, it is necessary to screen the key temperature values ​​for each temperature data subsequence. Through this screening process, temperature values ​​that are important for system control and optimization are extracted from the original temperature data. These key values ​​represent important features in temperature changes. By randomly extracting the first temperature data subsequence from the temperature data subsequence cluster and building a screening space based on this, the system can more accurately locate and screen out key temperature points that are crucial to temperature control optimization. The central temperature screening point serves as the starting point of the screening process to help further refine the screening range. Through this method, the most representative temperature features can be effectively found in complex temperature data, thereby accurately optimizing the drug constant temperature control system.

[0036] Based on the central temperature screening point of the temperature screening space, key temperature value screening is performed in the temperature screening space according to a preset screening bandwidth to obtain a first key temperature value, and based on the same obtaining principle for obtaining the first key temperature value, subsequence key temperature value screening is performed on each temperature data subsequence in the temperature data subsequence cluster to obtain a subsequence key temperature value cluster.

[0037] After screening and serialization, the key temperature value sequence set obtained provides core data support for subsequent temperature analysis and system optimization. This process ensures that the system can analyze temperature fluctuations more efficiently and accurately, and provides a more precise decision-making basis for system optimization.

[0038] Further, the central temperature screening point of the temperature screening space is extracted, and the central temperature screening point is used as an index to perform key temperature value screening in the temperature screening space according to a preset screening bandwidth to obtain a first key temperature value. Step S300 of the embodiment of the present application further includes:

[0039] A straight line passing through the central temperature screening point and parallel to the abscissa axis of the temperature screening space is used as the initial screening straight line, wherein the temperature screening space is a two-dimensional space, the abscissa axis is time, and the ordinate axis is temperature value;

[0040] Constructing an initial screening neighborhood of the initial screening straight line based on a preset screening bandwidth, and counting the initial screening neighborhood density of the initial screening neighborhood;

[0041] The initial screening straight line is moved upward and downward respectively by the preset screening bandwidth to obtain an upward screening straight line and a downward screening straight line;

[0042] Based on the upward screening straight line and the downward screening straight line, respectively, an upward screening neighborhood and a downward screening neighborhood are constructed in combination with the preset screening bandwidth, and the density of the upward screening neighborhood and the density of the downward screening neighborhood are counted;

[0043] When the upward-moving screening neighborhood density and / or the downward-moving screening neighborhood density is greater than or equal to the initial screening neighborhood density, the upward-moving screening straight line is iterated upward according to the preset screening bandwidth and / or the downward-moving screening straight line is iterated downward according to the preset screening bandwidth until the preset number of iterations is met, and the target upward-moving screening straight line and / or the target downward-moving screening straight line are determined;

[0044] Based on the target upward screening line and / or the target downward screening line, a key temperature value screening is performed in the temperature screening space to obtain a first key temperature value.

[0045] Further, when the upward-moving screening neighborhood density and the downward-moving screening neighborhood density are less than the initial screening neighborhood density, the temperature data corresponding to the central temperature screening point is used as the first key temperature value.

[0046] Further, based on the target upward screening line and / or the target downward screening line, a key temperature value is screened in the temperature screening space to obtain a first key temperature value. Step S300 of the embodiment of the present application further includes:

[0047] Adding the temperature data corresponding to the target upward filter line and / or the target downward filter line into the key temperature value filter set;

[0048] The key temperature value corresponding to the maximum value in the key temperature value screening set is used as the first key temperature value.

[0049] In one embodiment, a straight line passing through the central temperature screening point and parallel to the abscissa axis of the temperature screening space is used as the initial screening straight line. The temperature screening space is a two-dimensional space, the abscissa axis is time, and the ordinate axis is temperature value.

[0050] The initial screening neighborhood refers to the area composed of temperature screening points whose distance to the initial screening straight line is a preset screening bandwidth. The range of the neighborhood is determined by the preset screening bandwidth. The number of temperature screening points in the initial screening neighborhood is counted to obtain the density of the initial screening neighborhood. The density of the initial screening neighborhood refers to the number of temperature data in the screening neighborhood, which reflects the distribution of temperature data in the neighborhood. The larger the density, the more concentrated the temperature data in the area, which may mean that the temperature changes in the area are more representative.

[0051] The initial screening straight line is moved upward and downward by the preset screening bandwidth respectively to obtain an upward screening straight line and a downward screening straight line. Further, based on the same principle as that of obtaining the initial screening neighborhood, neighborhood construction is performed according to the preset screening bandwidth and the upward screening straight line and the downward screening straight line to obtain the upward screening neighborhood and the downward screening neighborhood. The number of temperature screening points in the upward screening neighborhood and the downward screening neighborhood is counted respectively to obtain the upward screening neighborhood density and the downward screening neighborhood density.

[0052] Furthermore, the upward-moving screening neighborhood density and the downward-moving screening neighborhood density are compared with the initial screening neighborhood density. When the upward-moving screening neighborhood density and / or the downward-moving screening neighborhood density is greater than or equal to the initial screening neighborhood density, it indicates that the initial screening neighborhood is not the most densely distributed area in the temperature screening space, and further iteration is required.

[0053] Then, based on the upward iteration of the upwardly-moving screening line according to the preset screening bandwidth and / or the downward iteration of the downwardly-moving screening line according to the preset screening bandwidth, until the preset number of iterations is met, the straight line obtained in the last iteration is respectively used as the target upwardly-moving screening line and / or the target downwardly-moving screening line. Among them, the preset number of iterations is the maximum number of iterations pre-set by a person skilled in the art. The temperature data corresponding to the target upwardly-moving screening line and / or the target downwardly-moving screening line is added to the key temperature value screening set, and the key temperature value corresponding to the maximum value in the key temperature value screening set is used as the first key temperature value. Thereby, it is ensured that the first key temperature value obtained can best represent the temperature value size of the first temperature data subsequence.

[0054] Furthermore, when the upward screening neighborhood density and the downward screening neighborhood density are less than the initial screening neighborhood density, it indicates that the temperature value corresponding to the central temperature screening point can best represent the temperature value of the first temperature data subsequence. Therefore, the temperature data corresponding to the central temperature screening point is used as the first key temperature value.

[0055] By dynamically adjusting the screening line in the screening space (moving up or down), more representative key temperature values ​​can be finely screened. In this process, the initial screening line is formed by intersecting the horizontal and vertical coordinates in the temperature screening space, ensuring that the starting point of the screening is located in the core area of ​​the temperature data. Subsequently, the system expands or narrows the screening range by moving the screening line up and down, and screens the temperature data based on the screening bandwidth. In the screening process, the system counts the density of each screening neighborhood, and the size of the density can reflect the density of the temperature data in the screening area. By continuously adjusting the position of the screening line and deciding whether to continue iterating based on the change in the density, this adaptive screening method can effectively find the key change area of ​​the temperature data. Through this iterative process, the system can select the most representative temperature data point based on the changes in the screening neighborhood, thereby obtaining the first key temperature value. The technical effect of obtaining a reliable first key temperature value and providing a basis for the precise control of the drug constant temperature system has been achieved.

[0056] S400: Based on the position of each temperature sensor in the temperature sensor array, perform simultaneous topological processing on the key temperature value sequence set to construct a key temperature value topological network sequence;

[0057] In a possible embodiment, the key temperature value topological network sequence is a network sequence that reflects the temperature change of the constant temperature area of ​​the target drug after topological processing. The topological network sequence combines the spatial and temporal relationship of the temperature data to form a network structure, in which each node represents a temperature sensor. This network can show the propagation characteristics of temperature changes in space and their mutual influence in time.

[0058] The position of each sensor represents a spatial coordinate. Combining these coordinate information with the temperature data sequence, the system can construct a topological structure of temperature changes in space. By topologically processing these sequences, a network sequence that reflects the temperature change law in the constant temperature area of ​​the drug can be obtained.

[0059] Optionally, extract the key temperature value set corresponding to the first time window in the key temperature value sequence set, and connect each key temperature value to each other according to the position of the corresponding temperature sensor to obtain a key temperature value topological network. Based on the above network construction process, perform simultaneous topological processing on the key temperature value sequence set to obtain a key temperature value topological network sequence.

[0060] The significance of this simultaneous topological processing is that it not only considers the temporal order of temperature changes, but also reveals the temperature distribution relationship in space through the location of the sensor. This topological network sequence can effectively represent the mutual influence of temperature changes between different locations, achieving the technical effect of providing accurate spatial and temporal data support for further control optimization.

[0061] S500: performing iterative correlation analysis on the key temperature value topological network sequence in chronological order to determine iterative correlation temperature characteristics of the constant temperature area of ​​the target drug within a preset time period;

[0062] Further, as attached Figure 2 As shown, the key temperature value topological network sequence is iteratively correlated and analyzed in chronological order to determine the iterative correlation temperature characteristics of the constant temperature area of ​​the target drug within a preset time period. Step S500 of the embodiment of the present application also includes:

[0063] Extracting a first key temperature value topological network and a second key temperature value topological network of the key temperature value topological network sequence in a time-ordered manner;

[0064] Respectively calculating the network node similarities of the first critical temperature value topology network and the second critical temperature value topology network to obtain a first network node similarity set, wherein each network node similarity is the degree of similarity of the critical temperature values ​​of the network nodes at the same position in the first critical temperature value topology network and the second critical temperature value topology network;

[0065] Performing matrix processing on the first network node similarity set to obtain a first iterative association matrix;

[0066] Performing convolution calculation on the first iterative association matrix and the second key temperature value topological network to obtain a first iterative association temperature value topological network;

[0067] Extracting a third key temperature value topological network of the key temperature value topological network sequence, performing iterative correlation analysis on the third key temperature value topological network and the first iterative correlation temperature value topological network to obtain a second iterative correlation temperature value topological network;

[0068] Perform multiple iterations of association in sequence until the iteration association analysis of the key temperature value topological network sequence is completed, obtain the target iteration association temperature value topological network, perform feature extraction on the target iteration association temperature value topological network, and obtain the iteration association temperature feature.

[0069] Furthermore, step S500 of the embodiment of the present application further includes:

[0070] A similarity normalization function is pre-constructed, wherein the similarity normalization function is:

[0071]

[0072] Among them, Nor[lim(x i ,y i )] is the normalized value corresponding to the i-th first network node similarity in the first network node similarity set, e is the base of the natural logarithm, m is the total number of first network node similarities in the first network node similarity set, lim(x i ,y j ) is the first network node similarity between the ith key temperature value in the first key temperature value topological network and the ith key temperature value at the same position in the second key temperature value topological network;

[0073] The first network node similarity set is normalized using the similarity normalization function, and the processing result is embedded in an initially empty matrix to obtain the first iterative association matrix.

[0074] In one embodiment, each key temperature value topological network in the key temperature value topological network sequence reflects the temperature distribution of the target drug constant temperature area within a time window. By iteratively correlating the key temperature value topological network sequence in chronological order, the temperature trend change between different time windows is mined to obtain the temperature characteristics that can reflect the target drug constant temperature area within a preset time period, that is, the iterative correlation temperature characteristics. The technical effect of providing reliable data support for subsequent accurate drug constant temperature system control optimization is achieved.

[0075] Optionally, the first key temperature value topology network and the second key temperature value topology network of the key temperature value topology network sequence are extracted in chronological order. The network node similarities of the first key temperature value topology network and the second key temperature value topology network are respectively calculated to obtain a first network node similarity set, wherein each network node similarity is the degree of similarity of the key temperature values ​​of the network nodes at the same position in the first key temperature value topology network and the second key temperature value topology network.

[0076] Preferably, the first key temperature value and the second key temperature value at the same position in the first key temperature value topology network and the second key temperature value topology network are extracted, the difference between the first key temperature value and the second key temperature value is calculated, and the inverse of the calculation result is used as the first network node similarity. Based on the above principle, the first key temperature value and the second key temperature value at the same position in the first key temperature value topology network and the second key temperature value topology network are similarity calculated to obtain the first network node similarity set.

[0077] Optionally, the first iterative association matrix is ​​obtained by performing matrix processing on the first network node similarity set and analyzing the association relationship between the first key temperature value topological network and the second key temperature value topological network.

[0078] Preferably, a similarity normalization function is pre-constructed, wherein the similarity normalization function is:

[0079]

[0080] Among them, Nor[lim(x i ,y i )] is the normalized value corresponding to the i-th first network node similarity in the first network node similarity set, e is the base of the natural logarithm, m is the total number of first network node similarities in the first network node similarity set, lim(x i ,y j ) is the first network node similarity between the ith key temperature value in the first key temperature value topological network and the ith key temperature value at the same position in the second key temperature value topological network.

[0081] The similarity normalization function is used to normalize the similarity of the first network node in the first network node similarity set, and embed the processing result into an initially empty matrix to obtain the first iterative association matrix. The first iterative association matrix reflects the association relationship between the first key temperature value topological network and the second key temperature value topological network.

[0082] Furthermore, a graph convolutional network is used to perform convolution calculations on the first iterative association matrix and the second key temperature value topological network, and an impact analysis is performed on the second key temperature value topological network in combination with the association relationship to identify more detailed temperature changes, thereby obtaining the first iterative association temperature value topological network.

[0083] Preferably, multiple sample iterative association matrices, multiple sample key temperature value topological networks and corresponding multiple sample iterative associated temperature value topological networks are obtained as training data, and the training data is used to perform supervised training on a framework built based on a convolutional neural network to learn the two-to-one mapping relationship between the iterative association matrix and the key temperature value topological network and the iterative associated temperature value topological network, so as to obtain a trained graph convolutional network.

[0084] The third key temperature value topological network of the key temperature value topological network sequence is extracted again, and iterative association analysis is performed on it and the first iterative association temperature value topological network. Based on the same principle as the first iterative association temperature value topological network, the second iterative association temperature value topological network is obtained. Multiple iterative associations are performed in sequence until the iterative association analysis of the key temperature value topological network sequence is completed, and the target iterative association temperature value topological network is obtained. The target iterative association temperature value topological network is feature extracted using a convolutional neural network to obtain the iterative association temperature feature.

[0085] By iteratively analyzing the topological network sequence of key temperature values, the temperature change characteristics of the constant temperature area of ​​the target drug within the preset time period are revealed. This process can mine the temperature fluctuation rules from the temperature data of multiple time nodes and identify potential problems that may exist in the temperature control system, such as abnormal temperature fluctuations and local temperature out of control. The technical effect of providing data support for subsequent reliable system control optimization is achieved.

[0086] S600: Control optimization of the medicine constant temperature system is performed based on the iterative correlation temperature characteristics to obtain control optimization parameters, wherein the medicine constant temperature system is used to control the temperature of the target medicine constant temperature area.

[0087] Further, based on the iterative correlation temperature characteristics, the medicine constant temperature system is controlled and optimized to obtain control optimization parameters. Step S600 of the embodiment of the present application also includes:

[0088] Obtaining an iterative correlation temperature feature sample set and a control optimization parameter sample set;

[0089] Performing training based on the iterative correlation temperature feature sample set and the control optimization parameter sample set to obtain a control optimization parameter identifier;

[0090] The iterative correlation temperature feature is identified by using the control optimization parameter identifier to obtain the control optimization parameter.

[0091] In one embodiment, the iteratively correlated temperature feature sample set is composed of iteratively correlated temperature features in multiple time periods, and each sample corresponds to a temperature change pattern in a time period. The control optimization parameter sample set contains control optimization parameters corresponding to each iteratively correlated temperature feature sample. The control optimization parameters include various adjustment parameters of the temperature control system, such as the working intensity of the refrigeration or heating equipment, the speed of the fan, the power of the heating tube, etc. Among them, the drug constant temperature system is used to control the temperature of the target drug constant temperature area.

[0092] The machine learning model (such as neural network, support vector machine, decision tree, etc.) is trained using the iterative correlation temperature feature sample set and the control optimization parameter sample set. The purpose of the training is to enable the model to predict the corresponding control optimization parameters based on the input temperature change characteristics (iterative correlation temperature characteristics). Through training, a mapping relationship between iterative correlation temperature characteristics and control optimization parameters is established. This process continuously adjusts the parameters in the model through optimization algorithms (such as minimizing errors) to ensure that the prediction of the control parameters is as accurate as possible.

[0093] After the model training is completed, the iteratively associated temperature features are input, and these features are identified using the trained control optimization parameter identifier to predict the corresponding control optimization parameters. This process is carried out in real time and can automatically adjust the control parameters of the drug constant temperature system according to the changing temperature data. According to the identification results, the corresponding control optimization parameters are output, and these parameters will guide the temperature control system to make real-time adjustments. Through this process, the temperature control system can adapt to changes in ambient temperature and maintain the temperature stability of the target drug constant temperature area.

[0094] The system will provide feedback on the temperature change based on the results of each control optimization. If the temperature remains within the desired range, the control optimization parameters can continue to be used. If the temperature change exceeds the preset range, the optimization process will be repeated to adjust the control optimization parameters. That is, by continuously monitoring and adjusting the working status of the drug constant temperature system, the continuous stability of temperature control is ensured.

[0095] By combining machine learning with control optimization strategies, the control parameters of the drug constant temperature system are dynamically optimized based on iterative correlation temperature characteristics. This method can not only improve the timeliness of system response, but also improve the accuracy and stability of temperature control, helping to ensure the quality and safety of drugs.

[0096] In summary, the embodiments of the present application have at least the following technical effects:

[0097] 1. This application achieves the technical effect of improving the response speed and accuracy of the drug constant temperature system and ensuring temperature stability through iterative correlation temperature feature analysis, accurate identification of temperature fluctuation patterns, and optimization of control parameters.

[0098] 2. This application optimizes control parameters through machine learning training, and the drug constant temperature system can automatically adjust to cope with different environmental changes, achieving the technical effect of enhancing the system's adaptability to external changes.

[0099] 3. This application achieves the technical effect of improving data analysis efficiency by performing dimensionality reduction processing on temperature data.

[0100] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0101] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0102] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for optimizing the control of a medicine constant temperature system, characterized in that: The method comprises: Acquire a pre-placed temperature sensor array of a target drug constant temperature area; Traversing the temperature sensor array to extract temperature data within a preset time period to obtain a temperature data sequence set, and presetting a fixed time window, using the preset fixed time window to perform sequence segmentation processing on the temperature data sequence set to obtain a temperature data subsequence cluster, wherein the temperature data subsequence cluster includes a temperature data subsequence set corresponding to each temperature data sequence; Performing subsequence key temperature value screening on each temperature data subsequence in the temperature data subsequence cluster respectively, and performing serialization processing on the screening results to obtain a key temperature value sequence set corresponding to the temperature data sequence set; Based on the position of each temperature sensor in the temperature sensor array, performing simultaneous topological processing on the key temperature value sequence set to construct a key temperature value topological network sequence; Performing iterative correlation analysis on the key temperature value topological network sequence in chronological order to determine iterative correlation temperature characteristics of the constant temperature area of ​​the target drug within a preset time period; Based on the iterative correlation temperature characteristics, the drug constant temperature system is controlled and optimized to obtain control optimization parameters, wherein the drug constant temperature system is used to control the temperature of the target drug constant temperature area.

2. A drug constant temperature system control optimization method as claimed in claim 1, characterized in that: Performing subsequence key temperature value screening on each temperature data subsequence in the temperature data subsequence cluster respectively, and performing serialization processing on the screening results to obtain a key temperature value sequence set corresponding to the temperature data sequence set, including: Randomly extracting a first temperature data subsequence from the temperature data subsequence cluster, and constructing a screening space based on the first temperature data subsequence to obtain a temperature screening space, wherein the temperature screening space includes a plurality of temperature screening points, and each temperature screening point corresponds to a temperature data; Extracting a central temperature screening point of the temperature screening space, taking the central temperature screening point as an index, screening a key temperature value in the temperature screening space according to a preset screening bandwidth, and obtaining a first key temperature value; Performing subsequence key temperature value screening on each temperature data subsequence in the temperature data subsequence cluster to obtain a subsequence key temperature value cluster; Each subsequence key temperature value set in the subsequence key temperature value cluster is serialized to obtain the key temperature value sequence set.

3. A drug constant temperature system control optimization method as claimed in claim 2, characterized in that: Extracting a central temperature screening point of the temperature screening space, taking the central temperature screening point as an index, screening a key temperature value in the temperature screening space according to a preset screening bandwidth, and obtaining a first key temperature value, including: A straight line passing through the central temperature screening point and parallel to the abscissa axis of the temperature screening space is used as the initial screening straight line, wherein the temperature screening space is a two-dimensional space, the abscissa axis is time, and the ordinate axis is temperature value; Constructing an initial screening neighborhood of the initial screening straight line based on a preset screening bandwidth, and counting the initial screening neighborhood density of the initial screening neighborhood; The initial screening straight line is moved upward and downward respectively by the preset screening bandwidth to obtain an upward screening straight line and a downward screening straight line; Based on the upward screening straight line and the downward screening straight line, respectively, an upward screening neighborhood and a downward screening neighborhood are constructed in combination with the preset screening bandwidth, and the density of the upward screening neighborhood and the density of the downward screening neighborhood are counted; When the upward-moving screening neighborhood density and / or the downward-moving screening neighborhood density is greater than or equal to the initial screening neighborhood density, the upward-moving screening straight line is iterated upward according to the preset screening bandwidth and / or the downward-moving screening straight line is iterated downward according to the preset screening bandwidth until the preset number of iterations is met, and the target upward-moving screening straight line and / or the target downward-moving screening straight line are determined; Based on the target upward screening line and / or the target downward screening line, a key temperature value screening is performed in the temperature screening space to obtain a first key temperature value.

4. A drug constant temperature system control optimization method as claimed in claim 3, characterized in that: When the upward-moving screening neighborhood density and the downward-moving screening neighborhood density are less than the initial screening neighborhood density, the temperature data corresponding to the central temperature screening point is used as the first key temperature value.

5. A method for optimizing the control of a medicine constant temperature system as claimed in claim 3, characterized in that: Performing key temperature value screening in the temperature screening space based on the target upward screening line and / or the target downward screening line to obtain a first key temperature value includes: Adding the temperature data corresponding to the target upward filter line and / or the target downward filter line into the key temperature value filter set; The key temperature value corresponding to the maximum value in the key temperature value screening set is used as the first key temperature value.

6. A drug constant temperature system control optimization method as claimed in claim 1, characterized in that: Performing iterative correlation analysis on the key temperature value topological network sequence in chronological order to determine iterative correlation temperature characteristics of the constant temperature area of ​​the target drug within a preset time period, including: Extracting a first key temperature value topological network and a second key temperature value topological network of the key temperature value topological network sequence in a time-ordered manner; Respectively calculating the network node similarities of the first critical temperature value topology network and the second critical temperature value topology network to obtain a first network node similarity set, wherein each network node similarity is the degree of similarity of the critical temperature values ​​of the network nodes at the same position in the first critical temperature value topology network and the second critical temperature value topology network; Performing matrix processing on the first network node similarity set to obtain a first iterative association matrix; Performing convolution calculation on the first iterative association matrix and the second key temperature value topological network to obtain a first iterative association temperature value topological network; Extracting a third key temperature value topological network of the key temperature value topological network sequence, performing iterative correlation analysis on the third key temperature value topological network and the first iterative correlation temperature value topological network to obtain a second iterative correlation temperature value topological network; Perform multiple iterations of association in sequence until the iteration association analysis of the key temperature value topological network sequence is completed, obtain the target iteration association temperature value topological network, perform feature extraction on the target iteration association temperature value topological network, and obtain the iteration association temperature feature.

7. A method for optimizing the control of a medicine constant temperature system as claimed in claim 6, characterized in that: include: A similarity normalization function is pre-constructed, wherein the similarity normalization function is: Among them, Nor[lim(x i ,y i )] is the normalized value corresponding to the i-th first network node similarity in the first network node similarity set, e is the base of the natural logarithm, m is the total number of first network node similarities in the first network node similarity set, lim(x i ,y i ) is the first network node similarity between the ith key temperature value in the first key temperature value topological network and the ith key temperature value at the same position in the second key temperature value topological network; The first network node similarity set is normalized using the similarity normalization function, and the processing result is embedded in an initially empty matrix to obtain the first iterative association matrix.

8. A method for optimizing the control of a medicine constant temperature system as claimed in claim 1, characterized in that: Based on the iterative correlation temperature characteristics, the drug constant temperature system is controlled and optimized to obtain control optimization parameters, including: Obtaining an iterative correlation temperature feature sample set and a control optimization parameter sample set; Performing training based on the iterative correlation temperature feature sample set and the control optimization parameter sample set to obtain a control optimization parameter identifier; The iterative correlation temperature feature is identified by using the control optimization parameter identifier to obtain the control optimization parameter.

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